Combination of biomechanical evaluation and accurate placement of dental implants: a new concept of virtual surgery in maxillary and mandibular functional reconstruction

Meta-analysis of the survival rate and postoperative infection rate of primary and secondary implants after vascularized fibula transplantation for reconstruction of jaw defects

OBJECTIVES

Vascularized fibula flap transplantation is the most effective and common method to repair the jaw defects. In addition, implantation is the first choice to restore dentition on the graft fibula. Implants are usually implanted at least 6 months after fibula transplantation. Primary implantation of implants during surgery can restore the dentition earlier, but whether this method can achieve the same restorative effect as secondary implantation is still uncertain. This article aims to compare the survival rate and complications between primary and secondary implantation through meta-analysis.

METHODS

This meta-analysis was conducted according to PRISMA protocol and the Cochrane Handbook of Systematic Reviews of Interventions. According to the inclusion and exclusion criteria, we selected the PubMed, Embase, Web of Science, Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), Chinese BioMedical Literature Database (CBM) according to established inclusion and exclusion criteria. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the included studies. Meta-analysis was conducted to compare the survival rate and postoperative infection rate of primary and secondary implantation.

RESULTS

Seven studies were involved in our research, involving 186 patients. Five of the studies detailed implant success in 106 patients (primary implantation 50, secondary implantation 56), and four studies documented infection after implantation in 117 patients (primary implantation 52, secondary implantation 65); the survival rate of the primary implantation was 93.3%, and the incidence of postoperative infection was 17.3%. The survival rate of the secondary implantation was 93.4%, and 23.1% had postoperative infection. Meta-analysis showed that there was no significant difference in the survival rate between primary implantation and secondary implantation, OR = 0.813 (95% CI 0.383-1.725, P = 0.589 > 0.05), and there was no significant difference in the incidence of postoperative infection, OR = 0.614 (95% CI 0.239-1.581, P = 0.312 > 0.05).

CONCLUSIONS

Based on the results of this study, the research found no significant difference in the survival rate or infection rates between primary and secondary implantation. After appropriate indications selection, primary implantation can be used to reconstruct the dentition with less waiting time, reduce the impact of radiotherapy, and bring a higher quality of life for patients.

Novel and accurate 3D-Printed surgical guide for mandibular reconstruction with integrated dental implants

PURPOSE

Patients with mandibular defects due to trauma or infiltrated disease are in a need of functional mandibular implants that will completely restore the function of their lower jaw. One of the most important roles of well-functioning jaw is mastication, a complex mechanism. A conventional approach used in oral and maxillofacial surgery accomplish this aim via two major surgeries- mandibular reconstruction and surgical placement of dental implants. Little work has been done on combining the two surgeries into with using Additive Manufacturing (AM) and digital planning.

MATERIAL AND METHODS

This case study offers a mandibular implant design solution with pre-positioned dental implants that can reduce the requirement to only one surgery. Mandibular implant was designed using 3-Matic software (Materialise, Belgium). Positions for dental implants were restoratively-driven and planned on the designed mandibular implant in Blue Sky Plan 4 software (Blue Sky Bio, USA) and placed prior to mandibular reconstruction using a 3D-printed surgical guide. Finite Element Analysis (FEA) was used to evaluate the mechanical behaviour of the 3D-printed surgical guide during dental implant placement.

RESULTS

The surgical guide was fabricated using SLA and stress distribution was evaluated in ANSYS Workbench FEM software (Ansys Inc Swanson, Houston, USA). Results showed that the designed surgical guide can withstand the forces occurring during the surgery.

CONCLUSION

The proposed method substantially reduces the surgical procedure and recovery time, increases the accuracy, and allows for a predictable restorative solution that can be visualised from the beginning.

A 3D segmentation network of mandible from CT scan with combination of multiple convolutional modules and edge supervision in mandibular reconstruction

Mandibular reconstruction is a very complex surgery that demands removing the tumor, which is followed by reconstruction of the defective mandible. Accurate segmentation of the mandible plays an important role in its preoperative planning. However, there are many segmentation challenges including the connected boundaries of upper and lower teeth, blurred condyle edges, metal artifact interference, and different shapes of the mandibles with tumor invasion (MTI). Those manual or semi-automatic segmentation methods commonly used in clinical practice are time-consuming and have poor effects. The automatic segmentation methods are mainly developed for the mandible without tumor invasion (Non-MTI) rather than MTI and have problems such as under-segmentation. Given these problems, this paper proposed a 3D automatic segmentation network of the mandible with a combination of multiple convolutional modules and edge supervision. Firstly, the squeeze-and-excitation residual module is used for feature optimization to make the network focused more on the mandibular segmentation region. Secondly, the multi atrous convolution cascade module is adapted to implement a multi-scale feature search to extract more detailed features. Considering that most mandibular segmentation networks ignore the boundary information, the loss function combining region loss and edge loss is applied to further improve the segmentation performance. The final experiment shows that the proposed network can segment Non-MTI and MTI quickly and automatically with an average segmentation time of 7.41s for a CT scan. In the meantime, it also has a good segmentation accuracy. For Non-MTI segmentation, the dice coefficient (Dice) reaches 97.98 ± 0.36%, average surface distance (ASD) reaches 0.061 ± 0.016 mm, and 95% Hausdorff distance (95HD) reaches 0.484 ± 0.027 mm. For Non-MTI segmentation, the Dice reaches 96.90 ± 1.59%, ASD reaches 0.162 ± 0.107 mm, and 95HD reaches 1.161 ± 1.034 mm. Compared with other methods, the proposed method has better segmentation performance, effectively improving segmentation accuracy and reducing under-segmentation. It can greatly improve doctor's segmentation efficiency and will have a promising application prospect in mandibular reconstruction surgery in the future.